Cloud DFS super master detector location systems and methods

ABSTRACT

This application relates to wireless networks and more specifically to systems and methods for determining the location of distributed radar detectors and selecting available channels free of radar signals from a plurality of radio frequency channels. One embodiment includes a cloud DFS super master and a radar detector communicatively coupled to the cloud DFS super master. The cloud DFS super master is programmed to receive the results of the scan for a radar signal from the radar detector and to generate integrated client device geolocation information. The cloud DFS super master is also programmed to determine a location for the radar detector based at least on the integrated client device geolocation information, and determine a radio channel free of the radar signal based at least on the location for the radar detector and the results of the scan for the radar signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of, and claims priority to, U.S. patent application Ser. No. 15/214,437 titled “CLOUD DFS SUPER MASTER SYSTEMS AND METHODS” and filed on Jul. 19, 2016, which claims priority to U.S. Provisional Patent Application No. 62/259,981 titled “CLOUD DFS SUPER MASTER SYSTEMS AND METHODS,” and filed on Nov. 25, 2015, the disclosures of which are hereby incorporated herein by reference in their entirety.

BACKGROUND

The present disclosure relates to wireless networks and more specifically to systems and methods for determining the location of distributed radar detectors and selecting available channels free of radar signals from a plurality of radio frequency channels. One embodiment includes a cloud DFS super master and a radar detector communicatively coupled to the cloud DFS super master. The cloud DFS super master is programmed to receive the results of the scan for a radar signal from the radar detector and to generate integrated client device geolocation information. The cloud DFS super master is also programmed to determine a location for the radar detector based at least on the integrated client device geolocation information, and determine a radio channel free of the radar signal based at least on the location for the radar detector and the results of the scan for the radar signal.

Wi-Fi networks are crucial to today's portable modern life. Wi-Fi is the preferred network in the growing Internet-of-Things (IoT). But, the technology behind current Wi-Fi has changed little in the last ten years. The Wi-Fi network and the associated unlicensed spectrum are currently managed in inefficient ways. For example, there is little or no coordination between individual networks and equipment from different manufacturers. Such networks generally employ primitive control algorithms that assume the network consists of “self-managed islands,” a concept originally intended for low density and low traffic environments. The situation is far worse for home networks, which are assembled in completely chaotic ad hoc ways. Further, with more and more connected devices becoming commonplace, the net result is growing congestion and slowed networks with unreliable connections.

Similarly, LTE-U networks operating in the same or similar unlicensed bands as 802.11 a/n/ac Wi-Fi suffer similar congestion and unreliable connection issues and will often create congestion problems for existing Wi-Fi networks sharing the same channels. Additional bandwidth and better and more efficient utilization of spectrum is key to sustaining the usefulness of wireless networks including the Wi-Fi and LTE-U networks in a fast growing connected world.

Devices operating in certain parts of the 5 GHz U-NII-2 band, known as the DFS bands or the DFS channels, require active radar detection. This function is assigned to a device capable of detecting radar known as a DFS master, which is typically an access point or router. The DFS master actively scans the DFS channels and performs a channel availability check (CAC) and periodic in-service monitoring (ISM) after the channel availability check. The channel availability check lasts 60 seconds as required by the Federal Communications Commission (FCC) Part 15 Subpart E and ETSI 301 893 standards. The DFS master signals to the other devices in the network (typically client devices) by transmitting a DFS beacon indicating that the channel is clear of radar. Although the access point can detect radar, wireless clients typically cannot. Because of this, wireless clients must first passively scan DFS channels to detect whether a beacon is present on that particular channel. During a passive scan, the client device switches through channels and listens for a beacon transmitted at regular intervals by the access point on an available channel.

Once a beacon is detected, the client is allowed to transmit on that channel. If the DFS master detects radar in that channel, the DFS master no longer transmits the beacon, and all client devices upon not sensing the beacon within a prescribed time must vacate the channel immediately and remain off that channel for 30 minutes. For clients associated with the DFS master network, additional information in the beacons (i.e. the channel switch announcement) can trigger a rapid and controlled evacuation of the channel. Normally, a DFS master device is an access point with only one radio and is able to provide DFS master services for just a single channel. Significant problems of the current approach include: (1) hidden nodes; (2) hidden radar; (3) false radar detections; (4) long delays in DFS channel switching at radar detection or false radar detection; (5) failure to support geo-fencing of areas of radar use based on external data; (6) underutilization of the DFS spectrum due to the dominance of private access points in the DFS spectrum; (7) interference between proximate LTE-U and Wi-Fi devices; and (8) lack of spectrum-use coordination between devices. The present systems and methods using a cloud DFS super master address these issues with prior art systems.

SUMMARY

The present disclosure relates to wireless networks and more specifically to systems and methods for determining the location of distributed radar detectors and selecting available channels free of radar signals from a plurality of radio frequency channels. One embodiment includes a cloud DFS super master and a radar detector communicatively coupled to the cloud DFS super master. The cloud DFS super master is programmed to receive the results of the scan for a radar signal from the radar detector and to generate integrated client device geolocation information. The cloud DFS super master is also programmed to determine a location for the radar detector based at least on the integrated client device geolocation information, and determine a radio channel free of the radar signal based at least on the location for the radar detector and the results of the scan for the radar signal.

Other embodiments and various examples, scenarios and implementations are described in more detail below. The following description and the drawings set forth certain illustrative embodiments of the specification. These embodiments are indicative, however, of but a few of the various ways in which the principles of the specification may be employed. Other advantages and novel features of the embodiments described will become apparent from the following detailed description of the specification when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The aforementioned objects and advantages of the present invention, as well as additional objects and advantages thereof, will be more fully understood herein after as a result of a detailed description of a preferred embodiment when taken in conjunction with the following drawings in which:

FIG. 1 illustrates portions of the 5 GHz Wi-Fi spectrum including portions that require active monitoring for radar signals.

FIG. 2 illustrates how an exemplary cloud-based intelligence engine or cloud DFS super master may interface with a conventional host access point, an agility agent (an autonomous DFS master or radar detector), and client devices.

FIG. 3 illustrates how an exemplary cloud-based intelligence engine or cloud DFS super master in a peer-to-peer network may interface with client devices and an agility agent (an autonomous DFS master or radar detector) independent of any access point.

FIG. 4 illustrates a method of performing a channel availability check phase and in-service monitoring phase in a DFS scanning operation with an agility agent to make multiple DFS channels simultaneously available for use using a time-division multiplexed sequential channel availability check followed by continuous in-service monitoring.

FIG. 5 illustrates a method of performing a channel availability check phase and in-service monitoring phase in a DFS scanning operation with an agility agent to make multiple DFS channels simultaneously available for use using a continuous sequential channel availability check followed by continuous in-service monitoring.

FIG. 6A illustrates a method of performing a channel availability check phase and in-service monitoring phase in a DFS scanning operation with an agility agent to make multiple DFS channels simultaneously available for use.

FIG. 6B illustrates an exemplary beacon transmission duty cycle and an exemplary radar detection duty cycle.

FIG. 7 illustrates an example in which an agility agent is connected to a host device and connected to a network via the host device.

FIG. 8 illustrates an example in which an agility agent is connected to a host device and connected to a network and a cloud intelligence engine or cloud DFS super master via the host device.

FIG. 9 illustrates an example in which an agility agent is connected to a host device and connected to a network and a cloud intelligence engine or cloud DFS super master via the host device.

FIG. 10 illustrates a method of performing a channel availability check and in-service monitoring.

FIG. 11 illustrates another method of performing a channel availability check and in-service monitoring.

FIG. 12 illustrates another method of performing a channel availability check and in-service monitoring.

FIG. 13 illustrates how multiple agility agents provide geographically distributed overlapping views of a radar emitter.

FIG. 14 illustrates in a control loop diagram how the cloud intelligence engine takes the spectrum data from each agility agent, and after storing and filtering the data, combines it with similar data from a plurality of other agility agents and cloud data from other sources.

FIGS. 15A and 15B illustrates the logical interface between the wireless agility agent, the cloud intelligence engine, and an access point (or similarly a small cell LTE-U base station).

FIG. 16A illustrates the hidden node problem where an access points or small cell base station is hidden from view of other access points or small cell base stations by topography, obstruction, distance or channel conditions.

FIG. 16B illustrates the hidden radar problem, where a radar emitter is unseen by an agility agent due to topography or obstruction.

FIG. 16C illustrates the hidden radar problem where a radar emitter is unseen by an agility agent due to distance.

FIG. 17 illustrates an exemplary embodiment of a cloud DFS super master system in which the cloud DFS super master is communicatively coupled to a plurality of sensors that detect radar signals in the DFS band and detect wireless traffic information and is communicatively coupled to one or more client devices.

FIG. 18 is an illustration of multiple radar detectors distributed throughout a building.

FIG. 19 further illustrates the building of FIG. 18 with distributed radar detectors.

FIG. 20 is an illustration of the location information that the cloud DFS super master receives from the radar detector.

FIG. 21 is an example of the cloud DFS super master using a boundary condition to determine the center or locus of the location information.

FIG. 22 is an illustration of more than one radar detector receiving location information from a client device.

FIG. 23 illustrates an exemplary method of using radar detectors and the coupled cloud DFS super master to determine the location of the radar detectors.

DETAILED DESCRIPTION

The present disclosure relates to wireless networks and more specifically to systems and methods for determining the location of distributed radar detectors and selecting available channels free of radar signals from a plurality of radio frequency channels. One embodiment includes a cloud DFS super master and a radar detector communicatively coupled to the cloud DFS super master. The cloud DFS super master is programmed to receive the results of the scan for a radar signal from the radar detector and to generate integrated client device geolocation information. The cloud DFS super master is also programmed to determine a location for the radar detector based at least on the integrated client device geolocation information, and determine a radio channel free of the radar signal based at least on the location for the radar detector and the results of the scan for the radar signal.

As used herein, a channel “free” of occupying signals may include a channel with occupying signals that are lower than a signal threshold including signal strength, quantity, or traffic. Some embodiments employ a cloud DFS super master to access additional bandwidth for wireless networks, such as IEEE 802.11 a/n/ac and LTE-U networks. The additional bandwidth is derived from channels that require avoidance of channels with occupying signals. For example, additional bandwidth is derived from special compliance channels that require radar detection, such as the DFS channels of the U-NII-2 bands, by employing multi-channel radar detection and in-service monitoring, and active channel selection controls.

FIG. 1 illustrates portions of the 5 GHz Wi-Fi spectrum 101. FIG. 1 shows the frequencies 102 and channels 103 that make up portions of the 5 GHz Wi-Fi spectrum 101. A U-NII band is a FCC regulatory domain for 5-GHz wireless devices and is part of the radio frequency spectrum used by IEEE 802.11 a/n/ac devices and by many wireless ISPs. It operates over four ranges. The U-NII-1 band 105 covers the 5.15-5.25 GHz range. The U-NII-2A band 106 covers the 5.25-5.35 GHz range. The U-NII-2A band 106 is subject to DFS radar detection and avoidance requirements. The U-NII-2C band 107 covers the 5.47-5.725 GHz range. The U-NII-2C band 107 is also subject to DFS radar detection and avoidance requirements. The U-NII-3 band 109 covers the 5.725 to 5.850 GHz range. Use of the U-NII-3 band 109 is restricted in some jurisdictions like the European Union and Japan.

When used in an 802.11 a/n/ac or LTE-U wireless network, the agility agent may function as an autonomous DFS master device. In contrast to conventional DFS master devices, the agility agent is not an access point or router, but rather is a standalone wireless device employing inventive scanning techniques described herein that provide DFS scan capabilities across multiple channels, enabling one or more access point devices and peer-to-peer client devices to exploit simultaneous multiple DFS channels. The standalone autonomous DFS master may be incorporated into another device such as an access point, LTE-U host, base station, cell, or small cell, media or content streamer, speaker, television, mobile phone, mobile router, software access point device, or peer to peer device but does not itself provide network access to client devices. In particular, in the event of a radar event, the enabled access point and clients or wireless device are able to move automatically, predictively and very quickly to another DFS channel.

FIG. 2 provides a detailed illustration of an exemplary system using a cloud DFS super master. As illustrated in FIG. 2, the agility agent 200 may control at least one access point or LTE-U small cell base station to dictate channel (e.g., a communication channel associated with the 5 GHz Wi-Fi spectrum 101, a communication channel associated with a 5.9 GHz spectrum, a communication channel associated with a 3.5 GHz spectrum, etc.) selection primarily by (a) signaling availability of one or more DFS channels by simultaneous transmission of one or more beacon signals; (b) transmitting a listing of both the authorized available DFS channels, herein referred to as a whitelist, and the prohibited DFS channels in which a potential radar signal has been detected, herein referred to as a blacklist, along with control signals and a time-stamp signal, herein referred to as a dead-man switch timer via an associated non-DFS channel; (c) transmitting the same signals as (b) over a wired medium such as Ethernet or serial cable; and (d) receiving control, coordination and authorized and preferred channel selection guidance information from the cloud intelligence engine 235. It is to be appreciated that the cloud intelligence engine 235 can be a set of cloud intelligence devices associated with cloud-based distributed computational resources. For example, the cloud intelligence engine 235 can be associated with multiple devices, multiple servers, multiple machines and/or multiple clusters. As discussed in more detail below, in some embodiments the cloud intelligence engine 235 acts as a cloud DFS super master for connected client devices. The agility agent 200 sends the time-stamp signal, or dead-man switch timer, with communications to ensure that the access points 218, 223 do not use the information, including the whitelist, beyond the useful lifetime of the information. For example, a whitelist will only be valid for a certain period of time. The time-stamp signal avoids using noncompliant DFS channels by ensuring that an access point will not use the whitelist beyond its useful lifetime. The system allows currently available access points without radar detection—which cannot operate in the DFS channels—to operate in the DFS channels by providing the radar detection required by the FCC or other regulatory agencies. In an embodiment, the agility agent 200 may send a status signal (e.g., a heartbeat signal) to the access point control agent 219 to indicate a current status and/or a current state of the agility agent 200. The status signal provided by the agility agent 200 may act as a dead-man switch (e.g., in response to a local failure). Therefore, the access point control agent 219 can safely operate on non-DFS channels. In certain implementations, authorized available DFS channels can be associated with a set of enforcement actions that are time limited (e.g., authorized DFS channels for a certain geographic region can become unavailable for a few hours, etc.).

The host access point 218 and any other access point devices 223 under control of the autonomous DFS master 200 typically have the control agent portion 219, 224 installed within their communication stack. For example, the host access point 218 may have an access point control agent portion 219, 224 installed within a communication stack of the host access point 218. Furthermore, the network access point 223 may also have an access point control agent portion 219, 224 installed within a communication stack of the network access point 223. The control agent 219, 224 is an agent that acts under the direction of the agility agent 200 to receive information and commands from the agility agent 200. The control agent 219, 224 acts on information from the agility agent 200. For example, the control agent 219, 224 listens for information like a whitelist or blacklist from the agility agent. If a radar signal is detected by the agility agent 200, the agility agent 200 communicates that to the control agent 219, 224, and the control agent 219, 224 acts to evacuate the channel immediately. The control agent can also take commands from the agility agent 200. For example, the host access point 218 and network access point 223 can offload DFS monitoring to the agility agent 200 as long as they can listen to the agility agent 200 and take commands from the agility agent regarding available DFS channels.

The host access point 218 is connected to a wide area network 233 and includes an access point control agent 219 to facilitate communications with the agility agent 200. The access point control agent 219 includes a security module 220 and agent protocols 221 to facilitate communication with the agility agent 200, and swarm communication protocols 222 to facilitate communications between agility agents, access points, client devices, and other devices in the network. The agility agent 200 connects to the cloud intelligence engine 235 via the host access point 218 and the wide area network 233. The access point may set up a secure communications tunnel to communicate with the cloud intelligence engine 235 through, for example, an encrypted control channel associated with the host access point 218 and/or an encrypted control API in the host access point 218. The agility agent 200 transmits information to the cloud intelligence engine 235 such as whitelists, blacklists, state information, location information, time signals, scan lists (for example, showing neighboring access points), congestion (for example, number and type of re-try packets), and traffic information. The cloud intelligence engine 235 communicates information to the agility agent 200 via the secure communications tunnel such as access point location (including neighboring access points), access point/cluster current state and history, statistics (including traffic, congestion, and throughput), whitelists, blacklists, authentication information, associated client information, and regional and regulatory information. The agility agent 200 uses the information from the cloud intelligence engine 235 to control the access points and other network devices.

The agility agent 200 may communicate via wired connections or wirelessly with the other network components. In the illustrated example, the agility agent 200 includes a primary radio 215 and a secondary radio 216. The primary radio 215 is for DFS and radar detection and is typically a 5 GHz radio. The agility agent 200 may receive radar signals, traffic information, and/or congestion information through the primary radio 215. And the agility agent 200 may transmit information such as DFS beacons via the primary radio 215. The second radio 216 is a secondary radio for sending control signals to other devices in the network and is typically a 2.4 GHz radio. The agility agent 200 may receive information such as network traffic, congestion, and/or control signals with the secondary radio 216. And the agility agent 200 may transmit information such as control signals with the secondary radio 216. The primary radio 215 is connected to a fast channel switching generator 217 that includes a switch and allows the primary radio 215 to switch rapidly between a radar detector 211 and beacon generator 212. The channel switching generator 217 allows the radar detector 211 to switch sufficiently fast to appear to be on multiple channels at a time. In certain implementations, the agility agent 200 may also include coordination 253. The coordination 253 may provide cross-network coordination between the agility agent 200 and another agility agent (e.g., agility agent(s) 251). For example, the coordination 253 may provide coordination information (e.g., precision location, precision position, channel allocation, a time-slice duty cycle request, traffic loading, etc.) between the agility agent 200 and another agility agent (e.g., agility agent(s) 251) on a different network. In one example, the coordination 253 may enable an agility agent (e.g., agility agent 200) attached to a Wi-Fi router to coordinate with a nearby agility (e.g., agility agent(s) 251) attached to a LTE-U small cell base station.

A standalone multi-channel DFS master may include a beacon generator 212 to generate a beacon in each of a plurality of radio channels (e.g., a plurality of 5 GHz communication channels, a plurality of 5.9 GHz communication channels, a plurality of 3.5 GHz communication channels, etc., for simplicity the following examples use 5 GHz), a radar detector 211 to scan for a radar signal in each of the plurality of 5 GHz radio channels, a 5 GHz radio transceiver 215 to transmit the beacon in each of the plurality of 5 GHz radio channels and to receive the radar signal in each of the plurality of 5 GHz radio channels, and a fast channel switching generator 217 coupled to the radar detector, the beacon generator, and the 5 GHz radio transceiver. The fast channel switching generator 217 switches the 5 GHz radio to a first channel of the plurality of 5 GHz radio channels and then causes the beacon generator 212 to generate the beacon in the first channel of the plurality of 5 GHz radio channels. Then the fast channel switching generator 217 causes the radar detector 211 to scan for the radar signal in the first channel of the plurality of 5 GHz radio channels. The fast channel switching generator 217 then repeats these steps for each other channel of the plurality of 5 GHz radio channels during a beacon transmission duty cycle and, in some examples, during a radar detection duty cycle. The beacon transmission duty cycle is the time between successive beacon transmissions on a given channel and the radar detection duty cycle which is the time between successive scans on a given channel. Because the agility agent 200 cycles between beaconing and scanning in each of the plurality of 5 GHz radio channels in the time window between a first beaconing and scanning in a given channel and a subsequent beaconing and scanning the same channel, it can provide effectively simultaneous beaconing and scanning for multiple channels.

The agility agent 200 also may contain a Bluetooth radio 214 and an 802.15.4 radio 213 for communicating with other devices in the network. The agility agent 200 may include various radio protocols 208 to facilitate communication via the included radio devices.

The agility agent 200 may also include a location module 209 to geo-locate or otherwise determine the location of the agility agent 200. Information provided by the location module 209 may be employed to location-tag and/or time-stamp spectral information collected and/or generated by the agility agent 200. As shown in FIG. 2, the agility agent 200 may include a scan and signaling module 210. The agility agent 200 includes embedded memory 202, including for example flash storage 201, and an embedded processor 203. The cloud agent 204 in the agility agent 200 facilitates aggregation of information from the cloud agent 204 through the cloud and includes swarm communication protocols 205 to facilitate communications between agility agents, access points, client devices, and other devices in the network. The cloud agent 204 also includes a security module 206 to protect and secure the agility agent's 200 cloud communications as well as agent protocols 207 to facilitate communication with the access point control agents 219, 224.

As shown in FIG. 2, the agility agent 200 may control other access points, for example networked access point 223, in addition to the host access point 218. The agility agent 200 may communicate with the other access points 223 via a wired or wireless connection 236, 237. In one example, the agility agent 200 may communicate with the other access points 223 via a local area network. The other access points 223 include an access point control agent 224 to facilitate communication with the agility agent 200 and other access points. The access point control agent 224 includes a security module 225, agent protocols 226 and swarm communication protocols 227 to facilitate communications with other agents (including other access points and client devices) on the network.

The cloud intelligence engine 235 includes a database 248 and memory 249 for storing information from the agility agent 200, other agility agents (not shown) connected to the intelligence engine 235, and external data sources. The database 248 and memory 249 allow the cloud intelligence engine 235 to store information over months and years received from agility agents and external data sources. The data source(s) 252 may be associated with a set of databases. Furthermore, the data source(s) 252 may include regulatory information (e.g., non-spectral information) such as, but not limited to, geographical information system (GIS) information, other geographical information, FCC information regarding the location of radar transmitters, FCC blacklist information, National Oceanic and Atmospheric Administration (NOAA) databases, Department of Defense (DoD) information regarding radar transmitters, DoD requests to avoid transmission in DFS channels for a given location, and/or other regulatory information.

The cloud intelligence engine 235 also includes processors 250 to perform the cloud intelligence operations described herein. The roaming and guest agents manager 238 in the cloud intelligence engine 235 provides optimized connection information for devices connected to agility agents that are roaming from one access point to other or from one access point to another network. The roaming and guest agents manager 238 also manages guest connections to networks for agility agents connected to the cloud intelligence engine 235. The external data fusion engine 239 provides for integration and fusion of information from agility agents with information from external data sources for example GIS information, other geographical information, FCC information regarding the location of radar transmitters, FCC blacklist information, NOAA databases, DoD information regarding radar transmitters, and DoD requests to avoid transmission in DFS channels for a given location. The cloud intelligence engine 235 further includes an authentication interface 240 for authentication of received communications and for authenticating devices and users. The radar detection compute engine 241 aggregates radar information from agility agents and external data sources and computes the location of radar transmitters from those data to, among other things, facilitate identification of false positive radar detections or hidden nodes and hidden radar. The radar detection compute engine 241 may also guide or steer multiple agility agents to dynamically adapt detection parameters and/or methods to further improve detection sensitivity. The location compute and agents manager 242 determines the location the agility agent 200 and other connected devices through Wi-Fi lookup in a Wi-Fi location database, querying passing devices, triangulation based on received signal strength indication (RSSI), triangulation based on packet time-of-flight, scan lists from agility agents, multiangulation, trilateration, multilateration, and/or geometric inference.

The spectrum analysis and data fusion engine 243 and the network optimization self-organization engine 244 facilitate dynamic spectrum optimization with information from the agility agents and external data sources. Each of the agility agents connected to the cloud intelligence engine 235 have scanned and analyzed the local spectrum and communicated that information to the cloud intelligence engine 235. The cloud intelligence engine 235 also knows the location of each agility agent and the access points proximate to the agility agents that do not have a controlling agent as well as the channel on which each of those devices is operating. With this information, the spectrum analysis and data fusion engine 243 and the network optimization self-organization engine 244 can optimize the local spectrum by telling agility agents to avoid channels subject to interference. The swarm communications manager 245 manages communications between agility agents, access points, client devices, and other devices in the network. The cloud intelligence engine includes a security manager 246. The control agents manager 247 manages all connected control agents. In an implementation, the cloud intelligence engine 235 may enable the host access point 218 to coordinate network configurations with same networks (e.g., Wi-Fi to Wi-Fi) and/or across different networks (e.g., Wi-Fi to LTE-U). Furthermore, the cloud intelligence engine 235 may enable agility agents (e.g., agility agent 200 and agility agent(s) 251) connected to different host access devices to communicate within a same network (e.g., Wi-Fi to Wi-Fi) and/or across a different network (e.g., Wi-Fi to LTE-U).

Independent of a host access point 218, the agility agent 200, in the role of an autonomous DFS master device, may also provide the channel indication and channel selection control to one or more peer-to-peer client devices 231, 232 within the coverage area by (a) signaling availability of one or more DFS channels by simultaneous transmission of one or more beacon signals; (b) transmitting a listing of both the authorized available DFS channels, herein referred to as a whitelist and the prohibited DFS channels in which a potential radar signal has been detected, herein referred to as a blacklist along with control signals and a time-stamp signal, herein referred to as a dead-man switch timer via an associated non-DFS channel; and (c) receiving control, coordination and authorized and preferred channel selection guidance information from the cloud intelligence engine 235. The agility agent 200 sends the time-stamp signal, or dead-man switch timer, with communications to ensure that the devices do not use the information, including the whitelist, beyond the useful lifetime of the information. For example, a whitelist will only be valid for a certain period of time. The time-stamp signal avoids using noncompliant DFS channels by ensuring that a device will not use the whitelist beyond its useful lifetime. Alternatively, the cloud intelligence engine 235 acting as a cloud DFS super master may provide available channels to the client devices.

Such peer-to-peer devices may have a user control interface 228. The user control interface 228 includes a user interface 229 to allow the client devices 231, 232 to interact with the agility agent 200 via the cloud intelligence engine 235. For example, the user interface 229 allows the user to modify network settings via the agility agent 200 including granting and revoking network access. The user control interface 228 also includes a security element 230 to ensure that communications between the client devices 231, 232 and the agility agent 200 are secure. The client devices 231, 232 are connected to a wide area network 234 via a cellular network for example. In certain implementations, peer-to-peer wireless networks are used for direct communication between devices without an access point. For example, video cameras may connect directly to a computer to download video or images files using a peer-to-peer network. Also, device connections to external monitors and device connections to drones currently use peer-to-peer networks. Therefore, in a peer-to-peer network without an access point, DFS channels cannot be employed since there is no access point to control DFS channel selection and/or to tell devices which DFS channels to use.

FIG. 3 illustrates how the agility agent 200 in a peer-to-peer network 300 (a local area network for example) would interface to client devices 231, 232, 331 and the cloud intelligence engine 235 independent of any access point. As shown in FIG. 3, the cloud intelligence engine 235 may be connected to a plurality of network-connected agility agents 200, 310. The agility agent 200 in the peer-to-peer network 300 may connect to the cloud intelligence engine 235 through one of the network-connected client devices 231, 331 by, for example, piggy-backing a message to the cloud intelligence engine 235 on a message send to the client devices 231, 331 or otherwise coopting the client devices' 231, 331 connection to the wide area network 234. In the peer-to-peer network 300, the agility agent 200 sends over-the-air control signals 320 to the client devices 231, 232, 331 including indications of channels free of occupying signals such as DFS channels free of radar signals. Alternatively, the agility agent communicates with just one client device 331 which then acts as the group owner to initiate and control the peer-to-peer communications with other client devices 231, 232. The client devices 231, 232, 331 have peer-to-peer links 321 through which they communicate with each other.

The agility agent may operate in multiple modes executing a number of DFS scan methods employing different algorithms. Two of these methods are illustrated in FIG. 4 and FIG. 5.

FIG. 4 illustrates a first DFS scan method 400 for a multi-channel DFS master. This method uses a time division sequential CAC 401 followed by continuous ISM 402. The method begins at step 403 with the multi-channel DFS master at startup or after a reset. At step 404 the embedded radio is set to receive (Rx) and is tuned to the first DFS channel (C=1). In one example, the first channel is channel 52. Next, because this is the first scan after startup or reset and the DFS master does not have information about channels free of radar, the DFS master performs a continuous CAC 405 scan for a period of 60 seconds (compliant with the FCC Part 15 Subpart E and ETSI 301 893 requirements). At step 406 the DFS master determines if a radar pattern is present in the current channel. If radar pattern is detected 407, then the DFS master marks this channel in the blacklist. The DFS master may also send additional information about the detected radar including the signal strength, radar pattern, type of radar, and a time stamp for the detection.

At the first scan after startup or reset, if a radar pattern is detected in the first channel scanned, the DFS master may repeat the above steps until a channel free of radar signals is found. Alternatively, after a startup or reset, the DFS master may be provided a whitelist indicating one or more channels that have been determined to be free of radar signals. For example, the DFS master may receive a message that channel 52 is free of radar signals from the cloud intelligence engine 235 along with information fused from other sources.

If at step 406 the DFS master does not detect a radar pattern 410, the DFS master marks this channel in the whitelist and switches the embedded radio to transmit (Tx) (not shown in FIG. 4) at this channel. The DFS master may include additional information in the whitelist including a time stamp. The DFS master then transmits (not shown in FIG. 4) a DFS master beacon signal for minimum required period of n (which is the period of the beacon transmission defined by IEEE 802.11 requirements, usually very short on the order of a few microseconds). A common SSID may be used for all beacons of our system.

For the next channel scan after the DFS master finds a channel free of radar, the DFS master sets the radio to receive and tunes the radio to the next DFS channel 404 (for example channel 60). The DFS master then performs a non-continuous CAC radar detection scan 405 for period of X, which is the maximum period between beacons allowable for a client device to remain associated with a network (P_(M)) less a period of n required for a quick radar scan and the transmission of the beacon itself (X=P_(M)−n) 408. At 411, the DFS master saves the state of current non-continuous channel state (S_(C)) from the non-continuous CAC scan so that the DFS master can later resume the current non-continuous channel scan at the point where the DFS master left off. Then, at step 412, the DFS master switches the radio to transmit and tunes to the first DFS channel (in this example it was CH 52), performs quick receive radar scan 413 (for a period of D called the dwell time) to detect radar 414. If a radar pattern is detected, the DFS master marks the channel to the blacklist 418. When marking the channel to the blacklist, the DFS master may also include additional information about the detected radar pattern including signal strength, type of radar, and a time stamp for the detection. The type of radar detected includes information such as burst duration, number of bursts, pulses per burst, burst period, scan pattern, pulse repetition rate and interval, pulse width, chirp width, beam width, scan rate, pulse rise and fall times, frequency modulation, frequency hopping rate, hopping sequence length, and pulses per hop.

If no radar pattern is detected, the DFS master transmits again 415 the DFS master beacon for the first channel (channel 52 in the example). Next, the DFS master determines if the current channel (C_(B)) is the last channel in the whitelist (W_(L)) 416. In the current example, the current channel, channel 52, is the only channel in the whitelist at this point. Then, the DFS master restores 417 the channel to the saved state from step 411 and switches the radio back to receive mode and tunes the radio back to the current non-continuous CAC DFS channel (channel 60 in the example) 404. The DFS master then resumes the non-continuous CAC radar scan 405 for period of X, again accommodating the period of n required for the quick scan and transmission of the beacon. This is repeated until 60 seconds of non-continuous CAC scanning is accumulated 409—in which case the channel is marked in the whitelist 410—or until a radar pattern is detected—in which case this channel is marked in the blacklist 407.

Next, the DFS master repeats the procedure in the preceding paragraph for the next DFS channel (for example channel 100). The DFS master periodically switches 412 to previous whitelisted DFS channels to do a quick scan 413 (for a period of D called the dwell time), and if no radar pattern detected, transmits a beacon 415 for period of n in each of the previously CAC scanned and whitelisted DFS channels. Then the DFS master returns 404 to resume the non-continuous CAC scan 405 of the current CAC channel (in this case CH 100). The period X available for non-continuous CAC scanning before switching to transmit and sequentially beaconing the previously whitelisted CAC scanned channels is reduced by n for each of the previously whitelisted CAC scanned channels, roughly X=P_(M)−n*(W_(L)) where W_(L) is the number of previously whitelisted CAC scanned channels. This is repeated until 60 seconds of non-continuous CAC scanning is accumulated for the current channel 409. If no radar pattern is detected the channel is marked in the whitelist 410. If a radar pattern is detected, the channel is marked in the blacklist 407 and the radio can immediately switch to the next DFS channel to be CAC scanned.

The steps in the preceding paragraph are repeated for each new DFS channel until all desired channels in the DFS band have been CAC scanned. In FIG. 4, step 419 checks to see if the current channel C is the last channel to be CAC scanned R. If the last channel to be CAC scanned R has been reached, the DFS master signals 420 that the CAC phase 401 is complete and begins the ISM phase 402. The whitelist and blacklist information may be communicated to the cloud intelligence engine where it is integrated over time and fused with similar information from other agility agents.

During the ISM phase, the DFS master does not scan the channels in the blacklist 421. The DFS master switches 422 to the first channel in the whitelist and transmits 423 a DFS beacon on that channel. Then the DFS master scans 424 the first channel in the whitelist for a period of D_(ISM) (the ISM dwell time) 425, which may be roughly P_(M) (the maximum period between beacons allowable for a client device to remain associated with a network) minus n times the number of whitelisted channels, divided by the number of whitelisted channels (D_(ISM)=(P_(M)−n*W_(L))/n). Then the DFS master transmits 423 a beacon and scans 424 each of the channels in the whitelist for the dwell time and then repeats starting at the first channel in the whitelist 422 in a round robin fashion for each respective channel. If a radar pattern is detected 426, the DFS master beacon for the respective channel is stopped 427, and the channel is marked in the blacklist 428 and removed from the whitelist (and no longer ISM scanned). The DFS master sends alert messages 429, along with the new whitelist and blacklist to the cloud intelligence engine. Alert messages may also be sent to other access points and/or client devices in the network.

FIG. 5 illustrates a second DFS scan method 500 for a multi-channel DFS master. This method uses a continuous sequential CAC 501 followed by continuous ISM 502. The method begins at step 503 with the multi-channel DFS master at startup or after a reset. At step 504 the embedded radio is set to receive (Rx) and is tuned to the first DFS channel (C=1). In this example, the first channel is channel 52. The DFS master performs a continuous CAC scan 505 for a period of 60 seconds 507 (compliant with the FCC Part 15 Subpart E and ETSI 301 893 requirements). If radar pattern is detected at step 506 then the DFS master marks this channel in the blacklist 508.

If the DFS master does not detect radar patterns, it marks this channel in the whitelist 509. The DFS master determines if the current channel C is the last channel to be CAC scanned R at step 510. If not, then the DFS master tunes the receiver to the next DFS channel (for example channel 60) 504. Then the DFS master performs a continuous scan 505 for full period of 60 seconds 507. If a radar pattern is detected, the DFS master marks the channel in the blacklist 508 and the radio can immediately switch to the next DFS channel 504 and repeat the steps after step 504.

If no radar pattern is detected 509, the DFS master marks the channel in the whitelist 509 and then tunes the receiver next DFS channel 504 and repeats the subsequent steps until all DFS channels for which a CAC scan is desired. Unlike the method depicted in FIG. 4, no beacon is transmitted between CAC scans of sequential DFS channels during the CAC scan phase.

The ISM phase 502 in FIG. 5 is identical to that in FIG. 4 described above.

FIG. 6A illustrates how multiple channels in the DFS channels of the 5 GHz band are made simultaneously available by use of an agility agent. FIG. 6A illustrates the process of FIG. 5 wherein the autonomous DFS Master performs the DFS scanning CAC phase 600 across multiple channels and upon completion of CAC phase, the autonomous DFS Master performs the ISM phase 601. During the ISM phase the DFS master transmits multiple beacons to indicate the availability of multiple DFS channels to nearby host and non-host (ordinary) access points and client devices.

FIG. 6A shows the frequencies 602 and channels 603 that make up portions of the DFS 5 GHz Wi-Fi spectrum. U-NII-2A 606 covers the 5.25-5.35 GHz range. U-NII-2C 607 covers the 5.47-5.725 GHz range. The first channel to undergo CAC scanning is shown at element 607. The subsequent CAC scans of other channels are shown at elements 608. And the final CAC scan before the ISM phase 601 is shown at element 609.

In the ISM phase 601, the DFS master switches to the first channel in the whitelist. In the example in FIG. 6A, each channel 603 for which a CAC scan was performed was free of radar signals during the CAC scan and was added to the whitelist. Then the DFS master transmits 610 a DFS beacon on that channel. Then the DFS master scans 620 the first channel in the whitelist for the dwell time. Then the DFS master transmits 611 a beacon and scans 621 each of the other channels in the whitelist for the dwell time and then repeats starting 610 at the first channel in the whitelist in a round robin fashion for each respective channel. If a radar pattern is detected, the DFS master beacon for the respective channel is stopped, and the channel is marked in the blacklist and removed from the whitelist (and no longer ISM scanned).

FIG. 6A also shows an exemplary waveform 630 of the multiple beacon transmissions from the DFS master to indicate the availability of the multiple DFS channels to nearby host and non-host (ordinary) access points and client devices.

FIG. 6B illustrates a beacon transmission duty cycle 650 and a radar detection duty cycle 651. In this example, channel A is the first channel in a channel whitelist. In FIG. 6B, a beacon transmission in channel A 660 is followed by a quick scan of channel A 670. Next a beacon transmission in the second channel, channel B, 661 is followed by a quick scan of channel B 671. This sequence is repeated for channels C 662, 672; D 663, 673; E 664, 674; F 665, 675; G 666, 676, and H 667, 677. After the quick scan of channel H 677, the DFS master switches back to channel A and performs a second beacon transmission in channel A 660 followed by a second quick scan of channel A 670. The time between starting the first beacon transmission in channel A and starting the second beacon transmission in channel A is a beacon transmission duty cycle. The time between starting the first quick scan in channel A and starting the second quick scan in channel A is a radar detection duty cycle. In order to maintain connection with devices on a network, the beacon transmission duty cycle should be less than or equal to the maximum period between the beacons allowable for a client device to remain associated with the network.

A standalone multi-channel DFS master may include a beacon generator 212 to generate a beacon in each of a plurality of 5 GHz radio channels, a radar detector 211 to scan for a radar signal in each of the plurality of 5 GHz radio channels, a 5 GHz radio transceiver 215 to transmit the beacon in each of the plurality of 5 GHz radio channels and to receive the radar signal in each of the plurality of 5 GHz radio channels, and a fast channel switching generator 217 and embedded processor 203 coupled to the radar detector, the beacon generator, and the 5 GHz radio transceiver. The fast channel switching generator 217 and embedded processor 203 switch the 5 GHz radio transceiver 215 to a first channel of the plurality of 5 GHz radio channels and cause the beacon generator 212 to generate the beacon in the first channel of the plurality of 5 GHz radio channels. The fast channel switching generator 217 and embedded processor 203 also cause the radar detector 211 to scan for the radar signal in the first channel of the plurality of 5 GHz radio channels. The fast channel switching generator 217 and embedded processor 203 then repeat these steps for each of the other channels of the plurality of 5 GHz radio channels. The fast channel switching generator 217 and embedded processor 203 perform all of the steps for all of the plurality of 5 GHz radio channels during a beacon transmission duty cycle which is a time between successive beacon transmissions on a specific channel and, in some examples, a radar detection duty cycle which is a time between successive scans on the specific channel.

The example in FIG. 7 illustrates systems and methods for selecting available channels free of occupying signals from a plurality of radio frequency channels. The system includes an agility agent 700 functioning as an autonomous frequency selection master that has both an embedded radio receiver 702 to detect the occupying signals in each of the plurality of radio frequency channels and an embedded radio transmitter 703 to transmit an indication of the available channels and an indication of unavailable channels not free of the occupying signals. The agility agent 700 is programmed to connect to a host device 701 and control a selection of an operating channel selection of the host device by transmitting the indication of the available channels and the indication of the unavailable channels to the host device. The host device 701 communicates wirelessly with client devices 720 and acts as a gateway for client devices to a network 710 such as the Internet, other wide area network, or local area network. The host device 701, under the control of the agility agent 700, tells the client devices 720 which channel or channels to use for wireless communication. Additionally, the agility agent 700 may be programmed to transmit the indication of the available channels and the indication of the unavailable channels directly to client devices 720.

The agility agent 700 may operate in the 5 GHz band and the plurality of radio frequency channels may be in the 5 GHz band and the occupying signals are radar signals. The host device 701 may be a Wi-Fi access point or an LTE-U host device.

Further, the agility agent 700 may be programmed to transmit the indication of the available channels by transmitting a channel whitelist of the available channels and to transmit the indication of the unavailable channels by transmitting a channel blacklist of the unavailable channels. In addition to saving the channel in the channel blacklist, the agility agent 700 may also be programmed to determine and save in the channel blacklist information about the detected occupying signals including signal strength, traffic, and type of the occupying signals.

As shown in FIG. 8, the agility agent 700 may be connected to a cloud-based intelligence engine 855. The agility agent 700 may connect to the cloud intelligence engine 855 directly or through the host device 701 and network 710. The cloud intelligence engine 855 integrates time distributed information from the agility agent 700 and combines information from a plurality of other agility agents 850 distributed in space and connected to the cloud intelligence engine 855. The agility agent 700 is programmed to receive control and coordination signals and authorized and preferred channel selection guidance information from the cloud intelligence engine 755.

The example shown in FIG. 9 shows a system and method for selecting available channels free of occupying signals from a plurality of radio frequency channels in which an agility agent 700 functioning as an autonomous frequency selection master includes an embedded radio receiver 702 to detect the occupying signals in each of the plurality of radio frequency channels and an embedded radio transmitter 703 to indicate the available channels and unavailable channels not free of the occupying signals. The agility agent 700 contains a channel whitelist 910 of one or more channels scanned and determined not to contain an occupying signal. The agility agent 700 may receive the whitelist 910 from another device including a cloud intelligence engine 855. Or the agility agent 700 may have previously derived the whitelist 910 through a continuous CAC for one or more channels. In this example, the agility agent 700 is programmed to cause the embedded radio receiver 702 to scan each of the plurality of radio frequency channels non-continuously interspersed with periodic switching to the channels in the channel whitelist 910 to perform a quick occupying signal scan in each channel in the channel whitelist 910. The agility agent 700 is further programmed to cause the embedded radio transmitter 703 to transmit a first beacon transmission in each channel in the channel whitelist 910 during the quick occupying signal scan and to track in the channel whitelist 910 the channels scanned and determined not to contain the occupying signal during the non-continuous scan and the quick occupying signal scan. The agility agent 700 is also programmed to track in a channel blacklist 915 the channels scanned and determined to contain the occupying signal during the non-continuous scan and the quick occupying signal scan and then to perform in-service monitoring for the occupying signal, including transmitting a second beacon for each of the channels in the channel whitelist 910, continuously and sequentially.

FIG. 10 illustrates an exemplary method 1000 for selecting an operating channel from a plurality of radio frequency channels in an agility agent functioning as an autonomous frequency selection master. The method includes receiving a channel whitelist of one or more channels scanned and determined not to contain an occupying signal 1010. Next, the agility agent performs a channel availability check 1005 for the plurality of radio frequency channels in a time-division manner. The time-division channel availability check includes scanning 1010 with an embedded radio receiver in the agility agent each of the plurality of radio frequency channels non-continuously interspersed with periodic switching to the channels in the channel whitelist to perform a quick occupying signal scan and transmitting 1020 a first beacon with an embedded radio transmitter in the agility agent in each channel in the channel whitelist during the quick occupying signal scan. The agility agent also tracks 1030 in the channel whitelist the channels scanned in step 1010 and determined not to contain the occupying signal and tracks 1040 in a channel blacklist the channels scanned in step 1010 and determined to contain the occupying signal. Finally, the agility agent performs in-service monitoring for the occupying signal and a second beaconing transmission for each of the channels in the channel whitelist continuously and sequentially 1050.

FIG. 11 illustrates another exemplary method 1100 for selecting an operating channel from a plurality of radio frequency channels in an agility agent functioning as an autonomous frequency selection master. The method 1100 includes performing a channel availability check for each of the plurality of radio frequency channels by scanning 1101 with an embedded radio receiver in the agility agent each of the plurality of radio frequency channels continuously for a scan period. The agility agent then tracks 1110 in a channel whitelist the channels scanned and determined not to contain an occupying signal and tracks 1120 in a channel blacklist the channels scanned and determined to contain the occupying signal. Then the agility agent performs in-service monitoring for the occupying signal and transmits a beacon with an embedded radio transmitter in the agility agent for each of the channels in the channel whitelist continuously and sequentially 1130.

FIG. 12 illustrates a further exemplary method 1200 for selecting an operating channel from a plurality of radio frequency channels in an agility agent functioning as an autonomous frequency selection master. The method 1200 includes performing a channel availability check 1210 for each of the plurality of radio frequency channels and performing in-service monitoring and beaconing 1250 for each of the plurality of radio frequency channels. The channel availability check 1210 includes tuning an embedded radio receiver in the autonomous frequency selection master device to one of the plurality of radio frequency channels and initiating a continuous channel availability scan in the one of the plurality of radio frequency channels with the embedded radio receiver 1211. Next, the channel availability check 1210 includes determining if an occupying signal is present in the one of the plurality of radio frequency channels during the continuous channel availability scan 1212. If the occupying signal is present in the one of the plurality of radio frequency channels during the continuous channel availability scan, the channel availability check 1210 includes adding the one of the plurality of radio frequency channels to a channel blacklist and ending the continuous channel availability scan 1213. If the occupying signal is not present in the one of the plurality of radio frequency channels during the continuous channel availability scan during a first scan period, the channel availability check 1210 includes adding the one of the plurality of radio frequency channels to a channel whitelist and ending the continuous channel availability scan 1214. Next, the channel availability check 1210 includes repeating steps 1211 and 1212 and either 1213 or 1214 for each of the plurality of radio frequency channels.

The in-service monitoring and beaconing 1250 for each of the plurality of radio frequency channels includes determining if the one of the plurality of radio frequency channels is in the channel whitelist and if so, tuning the embedded radio receiver in the autonomous frequency selection master device to the one of the plurality of radio frequency channels and transmitting a beacon in the one of the plurality of radio frequency channels with an embedded radio transmitter in the autonomous frequency selection master device 1251. Next, the in-service monitoring and beaconing 1250 includes initiating a discrete channel availability scan (a quick scan as described previously) in the one of the plurality of radio frequency channels with the embedded radio receiver 1252. Next, the in-service monitoring and beaconing 1250 includes determining if the occupying signal is present in the one of the plurality of radio frequency channels during the discrete channel availability scan 1253. If the occupying signal is present, the in-service monitoring and beaconing 1250 includes stopping transmission of the beacon, removing the one of the plurality of radio frequency channels from the channel whitelist, adding the one of the plurality of radio frequency channels to the channel blacklist, and ending the discrete channel availability scan 1254. If the occupying signal is not present in the one of the plurality of radio frequency channels during the discrete channel availability scan for a second scan period, the in-service monitoring and beaconing 1250 includes ending the discrete channel availability scan 1255. Thereafter, the in-service monitoring and beaconing 1250 includes repeating steps 1251, 1252, and 1253 as well as either 1254 or 1255 for each of the plurality of radio frequency channels.

As discussed herein, the disclosed systems are fundamentally different from the current state of art in that: (a) the disclosed wireless agility agents enable multiple simultaneous dynamic frequency channels, which is significantly more bandwidth than provided by conventional standalone DFS master access points or small cell base stations; (b) the additional DFS channels may be shared with nearby (suitably equipped with a control agent) access points or small cells, enabling the network as a whole to benefit from the additional bandwidth; and (c) the selection of operating channels by the access points and/or small cell base stations can be coordinated by a centralized network organization element (the cloud intelligence engine) to avoid overlapping channels thus avoiding interference and relieving congestion.

The capability and functions in (a) to (c) are enabled by the centralized cloud intelligence engine which collects and combines the DFS radar and other spectrum information from each agility agent and geo-tags, stores, filters, and integrates the data over time, and combines it together by data fusion technique with information from a plurality of other agility agents distributed in space, and performs filtering and other post-processing on the collection with proprietary algorithms, and merges with other data from vetted sources (such as GIS, Federal Aviation Administration (FAA), FCC, and DoD databases, etc.).

Specifically, the cloud intelligence engine performs the following: continuously collects the spectrum, location and network congestion/traffic information from all wireless agility agents, the number and density of which grows rapidly as more access points and small cell base stations are deployed; continuously applying sophisticated filtering, spatial and time correlation and integration operations, and novel array-combining techniques, and pattern recognition, etc. across the data sets; applying inventive network analysis and optimization techniques to compute network organization decisions to collectively optimize dynamic channel selection of access points and small cell base stations across networks; and directing the adaptive control of dynamic channel selection and radio configuration of 802.11 a/n/ac access points and/or LTE-U small cell base stations via said wireless agility agents.

Agility agents, due to their attachment to Wi-Fi access points and LTE-U small cell base stations, are by nature deployed over wide geographical areas in varying densities and often with overlapping coverage. Thus the spectrum information collected by agility agents, in particular the signatures of DFS radar and congestion conditions of local networks, similarly represent multi-point overlapping measurements of the radio spectrum over wide areas, or viewed a different way, the information represents spectrum measurements by random irregular arrays of sensors measuring radar and sources of interference and/or congestion from different angles (see FIG. 13).

FIG. 13 illustrates how multiple agility agents 1311, 1312, 1313, 1314 (for example, each attached to an 802.11 a/n/ac Wi-Fi network) provide geographically distributed overlapping views (sets of sensor data) of a radar emitter 1350. The figure also shows how by reporting to the centralized cloud intelligence engine 235, the collective multiple view data when pieced together by the cloud intelligence engine 235 takes on the attributes of both spatial diversity (different range and fading/reflective channel conditions 1321, 1322, 1323, 1324) and angular diversity (for example, look angles 1331, 1332, 1333, 1334) all of which can thus be leveraged to generate a pseudo synthetic aperture view of the target radar 1350 or any other emitter source with considerably more effective gain and sensitivity than was represented by any single view from a single access point or small cell base station. Different positions 1321, 1322, 1323, 1324 and look angles 1331, 1332, 1333, 1334 results in different timing offset of received radar pulse train and different distortion of received signal due to different fading and reflective channel conditions. A subset of the agility agents 1311, 1312, 1313, 1314 may form a pseudo-synthetic antenna array that provides improved sensitivity to radar signals due to effective higher gain and robustness in radar detection due to redundancy. The data from the agility agents 1311, 1312, 1313, 1314 are transmitted to the cloud intelligence engine 235 which performs data correlation and integration to determine the location of the target radar 1350.

The cloud intelligence engine having considerable processing capabilities and infinitely scalable memory/storage, is able to store the time-stamped spectrum information from each agility agent over very long periods of time, thus enabling the cloud intelligence engine to also integrate and correlate the signatures of DFS radar and congestion conditions of the local network over time as well as over geographic space. Given a sufficient number of agility agents continuously acquiring spectral information over time, the cloud intelligence engine can construct an increasingly accurate and reliable spatial map of spectrum information in the 5 GHz band, including the presence or absence of radar signals. The spectral information may be location-tagged and/or time-stamped. The device may be, for example, an access point device, a DFS slave device, a peer-to-peer group owner device, a mobile hotspot device, a radio access node device or a dedicated sensor node device. With this information, client devices can directly query the cloud intelligence engine to find out what DFS channels are available and free of radar at the location of the client device. With this system, the client device no longer needs to wait for a beacon that would have otherwise been provided by an access point or agility agent as the client device can communicate with the cloud intelligence engine via a network connection to determine the available channels. In this situation, the cloud intelligence engine becomes a cloud DFS super master as it can provide DFS channel selection information for a plurality of client devices distributed over a wide range of geographies.

Further, the cloud intelligence engine is also able to access and combine data from other sources (data fusion), such as topographic and map information from GIS (Geographical Information System) servers, FCC databases, NOAA databases, etc. enabling the cloud intelligence engine to further compare, correlate, overlay and otherwise polish the baseline spectrum data from agility agents and augment the network self-organization algorithm to further improve the overall accuracy and robustness.

The cloud intelligence engine having thus formed a detailed picture of the dynamic spectrum conditions of 802.11 a/n/ac and LTE-U networks is able to use this data to compute optimal network configurations, in particular the selection of operating channels (in both DFS and non-DFS bands) and radio parameters, of individual access points and/or small cell base stations to avoid overlap with other nearby access points or base stations, interferers, and noisy or congested channels. The overall system embodied by this can thus be viewed as a large wide-area closed control system, as illustrated in FIG. 14.

In one example, a system includes a cloud DFS super master and a plurality of radar detectors communicatively coupled to the cloud DFS super master. The radar detectors are programmed to scan for a radar signal in each of a plurality of 5 GHz radio channels, to transmit the results of the scan for the radar signal to the cloud DFS super master, and to transmit geo-location information for each of the plurality of radar detectors to the cloud DFS super master. The cloud DFS super master is programmed to receive the results of the scan for the radar signal from each of the plurality of radar detectors and the geo-location information for the plurality of radar detectors and determine if a first radar detector of the plurality of radar detectors detected the radar signal in a first channel of the plurality of 5 GHz radio channels. If the cloud DFS super maser determines that the radar signal is present in the first channel, the cloud DFS super master is programmed to determine one or more radar detector (e.g., second radar detectors) of the plurality of radar detectors to evaluate the first radar detector's detection of the radar signal in the first channel based on the geo-location information for the first radar detector and the geo-location for the second radar detector. In one example, the cloud DFS super master is programmed to cause the one or more second radar detectors to switch to the first channel and scan for radar in the first channel. And in another example, the cloud DFS super master is programmed to cause the one or more second radar detectors increase a dwell time in the first channel. In these examples, the cloud DFS super master can coordinate the radar detectors when any one detector sees radar. The cloud DFS super master and network of radar detectors acts like a large synthetic aperture array, and the cloud DFS super master can control the radar detectors to take action. Some of the actions include moving one or more radar detector to the channel in which radar was detected and looking for radar or causing one or more radar detectors to dwell longer in the channel in which radar was detected. The more sensors looking at the radar signal, the better the radar signal can be characterized. Further, through geo-location the cloud DFS supertaster may determine that there are other detectors in a better position to measure or characterize the radar and may use data from one or more detectors (e.g., fusing data from multiple detectors). This could be driven by historical data or by knowing the type/model of detectors. Indeed, as sensors are upgraded their sensitivity may be better than previous generation of products. The cloud DFS supertaster may track what detectors (and their capabilities) are deployed in a given area and optimally select which ones will provide the secondary verifying radar scans.

FIG. 14 illustrates in a control loop diagram how the cloud intelligence engine takes the spectrum data (radar lists and patterns, whitelists, blacklists, RSSI, noise floor, nearest neighbors, congestion & traffic signatures, etc.) from a network of agility agents (e.g., each of the global network of agility agents 1410), and after storing (in storage 1425) and filtering the data, combines them with similar data from an agility agent 1411, cloud data 1420 from other sources (such as the GIS, FCC, FAA, DoD, NOAA, etc.), and user input 1435. Then applying the data to the network self-organization compute process 1426, the control loop performs optimum dynamic channel selection 1455 for each of the 802.11 a/n/ac access points or LTE-U small cell base stations in the network(s) and under control of an example system. In this way, the cloud intelligence engine tells the agility agent 1411 to change to the selected channel 1455 for the access point (using access point control 1412) from the current channel 1456 (the channel previously used by the access point). In contrast, conventional access points and small cell base stations behave as open control loops with limited single-source sensor input and without the benefit of the cloud intelligence engine to close the control loop.

Information (including spectral and location information) from the agility agent 1411 is used with information from a location database 1451 to resolve the location 1450 of the agility agent 1411 and the 802.11 a/n/ac access points or LTE-U small cell base stations in the network(s) and under control of the agility agent 1411. The lookup 1441 accesses stored data from the agility agents 1410. This information can be combined with the information from the resolve location step 1450 for geometric extrapolation 1442 of spectral conditions applicable for agility agent 1411 and the 802.11 a/n/ac access points or LTE-U small cell base stations in the network(s) and under control of the agility agent 1411.

As illustrated in FIG. 14, the control loop includes time integration of data 1445 from the agility agents 1411, spatial integration of data 1444 from the agility agents 1411, and fusion 1430 with data from other sources and user input 1435 to make an operating channel selection 1455 for agility agent 1411. As shown, the control loop also may include buffers 1447, 1449 (temporal), 1443 (spatial), 1446 (temporal) and filters 1448 as needed. The other agility agents 1410 may also have their own control loops similar to that illustrated in FIG. 14.

As previously discussed, the agility agent transmits information to the cloud intelligence engine including information about the detected radar pattern including signal strength, type of radar, and a time stamp for the detection. The type of radar detected includes information such as burst duration, number of bursts, pulses per burst, burst period, scan pattern, pulse repetition rate and interval, pulse width, chirp width, beam width, scan rate, pulse rise and fall times, frequency modulation, frequency hopping rate, hopping sequence length, and pulses per hop. The cloud intelligence engine uses this information to improve its false detection algorithms. For example, if an agility agent detects a particular radar type that it knows cannot be present in a certain location, the cloud intelligence engine can use that information in it probability algorithm for assessing the validity of that signal. The agility agent may transmit information to the cloud intelligence engine via an access point or via a client device as shown in FIG. 2.

Because the cloud intelligence engine has location information for the attached radar sensors, when the cloud intelligence engine receives a radar detection signal from one sensor, the cloud intelligence engine may use the location information for that sensor to verify the signal. The cloud intelligence engine may determine nearby sensors in the vicinity of the first sensor that detected the radar signal and search for the whitelist/blacklist channel history in the other sensors, and if the nearby sensors have current and sufficient information, the cloud intelligence engine may validate or invalidate the original radar detection from the first sensor.

Alternatively, the cloud intelligence engine or the first sensor may instruct nearby sensors (either through the cloud or locally) to focus on the detected channel and report their whitelist and blacklist back to the cloud. If the nearby sensors have current and sufficient information, the cloud intelligence engine may validate or invalidate the original radar detection from the first sensor. Further, based on the location information for the first sensor, the cloud intelligence engine may direct other nearby sensors to modify their scan times or characteristics or signal processing to better detect the signal detected by the first sensor.

FIGS. 15A and 15B illustrates the logical interface between the wireless agility agent, the cloud intelligence engine, and an access point (or similarly a small cell LTE-U base station). In particular this figure illustrates examples of the signaling and messages that can be exchanged between the agility agent and the cloud intelligence engine, and between the cloud intelligence engine and an access point (via the agility agent) during the phases of DFS scan operations, In-Service Monitoring (ISM) and when a radar event occurs forcing a channel change.

FIG. 15A illustrates an interface between the cloud intelligence engine 235, the agility agent 200 and the host access point 218. For example, signaling and/or messages may be exchanged between the cloud intelligence engine 235 and the agility agent 200. The signaling and/or messages between the cloud intelligence engine 235 and the agility agent 200 may be exchanged during a DFS scan operation, during an ISM operation and/or when a radar event occurs that results in changing of a radio channel. In an aspect, the signaling and/or messages between the cloud intelligence engine 235 and the agility agent 200 may be exchanged via a WAN (e.g., WAN 234) and/or a secure communication tunnel.

An authentication registration process 1502 of the cloud intelligence engine 235 may be associated with a message A. The message A may be exchanged between the cloud intelligence engine 235 and the agility agent 200. Furthermore, the message A may be associated with one or more signaling operations and/or one or more messages. The message A may facilitate an initialization and/or authentication of the agility agent 200. For example, the message may include information associated with the agility agent 200 such as, but not limited to, a unit identity, a certification associated with the agility agent 200, a nearest neighbors scan list associated with a set of other agility agents within a certain distance from the agility agent 200, service set identifiers, a received signal strength indicator associated with the agility agent 200 and/or the host access point 218, a maker identification associated with the host access point 218, a measured location (e.g., a global positioning system location) associated with the agility agent 200 and/or the host access point 218, a derived location associated with the agility agent 200 and/or the host access point 218 (e.g., derived via a nearby AP or a nearby client), time information, current channel information, status information and/or other information associated with the agility agent 200 and/or the host access point 218. In one example, the message A can be associated with a channel availability check phase.

A data fusion process 1504 of the cloud intelligence engine 235 may facilitate computation of a location associated with the agility agent 200 and/or the host access point 218. Additionally or alternatively, the data fusion process 1504 of the cloud intelligence engine 235 may facilitate computation of a set of DFS channel lists. The data fusion process 1504 may be associated with a message B and/or a message C. The message B and/or the message C may be exchanged between the cloud intelligence engine 235 and the agility agent 200. Furthermore, the message B and/or the message C may be associated with one or more signaling operations and/or one or more messages. The message B may be associated with spectral measurement and/or environmental measurements associated with the agility agent 200. For example, the message B may include information such as, but not limited to, a scanned DFS white list, a scanned DFS black list, scan measurements, scan statistics, congestion information, traffic count information, time information, status information and/or other measurement information associated with the agility agent 200. The message C may be associated with an authorized DFS, DFS lists and/or channel change. For example, the message C may include information such as, but not limited to, a directed (e.g., approved) DFS white list, a directed (e.g., approved) DFS black list, a current time, a list valid time, a computed location associated with the agility agent 200 and/or the host access point 218, a network heartbeat and/or other information associated with a channel and/or a dynamic frequency selection.

A network optimization process 1506 of the cloud intelligence engine 235 may facilitate optimization of a network topology associated with the agility agent 200. The network optimization process 1506 may be associated with a message D. The message D may be exchanged between the cloud intelligence engine 235 and the agility agent 200. Furthermore, the message D may be associated with one or more signaling operations and/or one or more messages. The message D may be associated with a change in a radio channel. For example, the message D may be associated with a radio channel for the host access point 218 in communication with the agility agent 200. The message D can include information such as, but not limited to, a radio channel (e.g., a command to switch to a particular radio channel), a valid time of a list, a network heartbeat and/or other information for optimizing a network topology.

A network update process 1508 of the cloud intelligence engine 235 may facilitate an update for a network topology associated with the agility agent 200. The network update process 1508 may be associated with a message E. The message E may be exchanged between the cloud intelligence engine 235 and the agility agent 200. Furthermore, the message E may be associated with one or more signaling operations and/or one or more messages. The message E may be associated with a network heartbeat and/or a DFS authorization. For example, the message E may include information such as, but not limited to, a nearest neighbors scan list associated with a set of other agility agents within a certain distance from the agility agent 200, service set identifiers, a received signal strength indicator associated with the agility agent 200 and/or the host access point 218, a maker identification associated with the host access point 218, a measured location update (e.g., a global positioning system location update) associated with the agility agent 200 and/or the host access point 218, a derived location update (e.g., derived via a nearby AP or a nearby client) associated with the agility agent 200 and/or the host access point 218, time information, current channel information, status information and/or other information. In one example, the message B, the message C, the message D and/or the message E can be associated with an ISM phase.

A manage DFS lists process 1510 of the agility agent 200 may facilitate storage and/or updates of DFS lists. The manage DFS lists process 1510 may be associated with a message F. The message F may be exchanged between the agility agent 200 and the host access point 218. In one example, the message F may be exchanged via a local area network (e.g., a wired local area network and/or a wireless local area network). Furthermore, the message F may be associated with one or more signaling operations and/or one or more messages. The message F may facilitate a change in a radio channel for the host access point 218. For example, the message F may include information such as, but not limited to, a nearest neighbors scan list associated with a set of other agility agents within a certain distance from the agility agent 200, service set identifiers, a received signal strength indicator associated with the agility agent 200 and/or the host access point 218, a maker identification associated with the host access point 218, a measured location update (e.g., a global positioning system location update) associated with the agility agent 200 and/or the host access point 218, a derived location update (e.g., derived via a nearby AP or a nearby client) associated with the agility agent 200 and/or the host access point 218, time information, current channel information, status information and/or other information. In one example, the message F may be associated with a cloud directed operation (e.g., a cloud directed operation where DFS channels are enabled).

FIG. 15B also illustrates an interface between the cloud intelligence engine 235, the agility agent 200 and the host access point 218. For example, FIG. 15B may provide further details in connection with FIG. 15A. As shown in FIG. 15B, signaling and/or messages may be exchanged between the cloud intelligence engine 235 and the agility agent 200. The signaling and/or messages between the cloud intelligence engine 235 and the agility agent 200 may be exchanged during a DFS scan operation, during ISM and/or when a radar event occurs that results in changing of a radio channel. In an aspect, the signaling and/or messages between the cloud intelligence engine 235 and the agility agent 200 may be exchanged via a WAN (e.g., WAN 234) and/or a secure communication tunnel.

As also shown in FIG. 15B, the network update process 1508 of the cloud intelligence engine 235 may facilitate an update for a network topology associated with the agility agent 200. The network update process 1508 may be associated with the message E. Then, a DFS list update process 1514 of the cloud intelligence engine 235 may facilitate an update to one or more DFS channel lists. The DFS list update process 1514 may be associated with a message G. The message G may be exchanged between the cloud intelligence engine 235 and the agility agent 200. In one example, the message G may be exchanged via a WAN (e.g., WAN 234) and/or a secure communication tunnel. Furthermore, the message G may be associated with one or more signaling operations and/or one or more messages. The message G may be associated with a radar event. For example, the message G may signal a radar event. Additionally or alternatively, the message G may include information associated with a radar event. For example, the message G may include information such as, but not limited to, a radar measurement channel, a radar measurement pattern, a time associated with a radar event, a status associated with a radar event, other information associated with a radar event, etc. The radar event may associated with one or more channels from a plurality of 5 GHz communication channels (e.g., a plurality of 5 GHz communication channels associated with the 5 GHz Wi-Fi spectrum 101). In one example, the message G can be associated with an ISM phase. The DFS list update process 1514 may also be associated with the message C.

Moreover, as also shown in FIG. 15B, the manage DFS lists process 1510 may be associated with the message F. The message F may be exchanged between the agility agent 200 and the host access point 218. A radar detection process 1516 of the agility agent 200 may detect and/or generate the radar event. Additionally, the radar detection process 1516 may notify the host access point 218 to change a radio channel (e.g., switch to an alternate radio channel). The message F and/or a manage DFS lists process 1512 may be updated accordingly in response to the change in the radio channel. In an aspect, signaling and/or messages may be exchanged between the cloud intelligence engine 235 and the host access point 218 during a DFS scan operation, during an ISM operation and/or when a radar event occurs that results in changing of a radio channel for the host access point 218.

In addition to traditional infrastructure network topologies (e.g., host Access point and clients and peer-to-peer networks or Wi-Fi-Direct), the present disclosures apply to extended infrastructure network topologies (e.g., mesh networks). For example, the host access points discussed herein could be a mesh peer participating in a mesh network and simultaneously providing infrastructure connectivity.

FIG. 16A illustrates the hidden node problem where an access points or small cell base station 1630 is hidden from view of other access points or small cell base stations 1631 by topography, obstruction, distance or channel conditions 1645. The hidden node problem is a particularly difficult issue with mesh networks or peer-to-peer sessions where these access points are communicating with each other; the hidden node 1630 may not detect the frame and would be unable to synchronize its network allocation vector (NAV). With this impairment the hidden node 1630 transmissions can potentially collide and interfere with communications between the other two nodes 1631, 1632. As shown in FIG. 16A, the agility agent 1650 reports scan lists to the cloud intelligence engine 1635 but cannot detect the hidden node 1630. Accordingly, the agility agent 1650 does not report the hidden node 1630 to the cloud intelligence engine 1635 in the reported scan lists. Agility agents 1651 associated with access points 1632 in neighboring networks also report scan lists to the cloud intelligence engine 1635. Because the hidden node 1630 may be detected by these agility agents 1651, the reported scan lists include the hidden node 1630. The cloud intelligence engine 1635 collects scan lists, from all agility agents 1650, 1651 including geographic information about the agility agents 1650, 1651. The cloud intelligence engine 1635 then determines the presence of the hidden node 1630 and reports the presence of the hidden node 1630 to agility agents 1650, 1651.

FIG. 16B illustrates the hidden radar problem, where a radar emitter 1660 is unseen by an agility agent 1653 due to topography or obstruction 1655. The hidden radar problem is a very serious concern of the FCC (and other regulators) because agility agent 1653 acting as a DFS master device for access points 1634 but not seeing the hidden radar 1660 may cause unintended interference. Agility agents 1652 near exposed nodes 1633 detect radar from a radar emitter 1660 and report to the cloud intelligence engine 1635 via an uplink back list message for example. The cloud intelligence engine 1635 informs agility agents 1653 near hidden nodes 1634 of the radar via a downlink black list message for example.

In some embodiments, an agility agent may be linked to multiple host access points. In one such possible configuration, a significant issue arises when the networking connection between the agility agent and an access point over Ethernet is long. FIG. 16C illustrates the hidden radar problem where a radar emitter is unseen by an agility agent due to distance. Networked nodes 1690, 1691, 1692 are far from a radar emitter 1675 and therefore do not detect the presence of radar signals. The nodes 1690, 1691, 1692 communicate this information to the agility agent 1670. The agility agent 1670 causes corresponding white lists and black lists to be broadcast wirelessly and over wired connections. A hidden node 1680 receives the lists from the agility agent 1670 but is in the presence of radar from the radar emitter 1675. The hidden node 1680 is separated from the agility agent 1670 by a long distance and is connected to the agility agent by a very long Ethernet connection 1681 for example.

Because the hidden node 1680 is distant from the agility agent 1670, its signature 1682 is not on the agility agent's 1670 scan list. Also, because the hidden node 1680 is too distant from the agility agent 1670, the hidden node 1680 cannot receive the wireless white list and/or black list from the agility agent 1670 or the time stamps of the wireless lists do not match those received via Ethernet when received by the hidden node 1680. To solve this problem, the white lists and/or black lists broadcast over wired Ethernet must match with the lists and timing broadcast over wireless in order for the node 1680 to use DFS channels. Also, the agility agent 1670 may broadcast list of authorized access points (e.g., 1690, 1691, 1692), and the control agent in the access point must see its SSID in the authorization list in order to use DFS channels. The agility agent 1670 only authorizes access points (e.g., 1690, 1691, 1692) which it sees by scan list and above a certain RSSI threshold. Access points 1680 who are not seen or have RSSI too low are deemed too far to use the agility agent's 1670 white list.

FIG. 16A-C illustrate how a cloud intelligence engine collecting data from a plurality of wireless agility agents proximal to the hidden node or hidden radar is able to discover the said hidden node or hidden radar. Any access point or small cell base station that is now aware that there is a hidden node to another access point on the same channel can now react to the node being hidden, and similarly any (and all) access points or small cells within probable range of a radar signal, even though hidden to some of the nodes, could be directly prevented from using a radar-occupied channel.

In one embodiment of a system using a cloud DFS super master, the cloud DFS super master receives information from a plurality of agility agents and/or access points. Additionally, because the cloud DFS super master provides the DFS channel information for client devices, some agility agents and access points will no longer need to transmit a beacon identifying available channels. In this situation, the system using a cloud DFS super master may include sensors that are radar detectors that perform the radar-sensing function of the agility agent described herein but do not transmit a beacon to identify the available channels.

The cloud DFS super master may provide the DFS super master function for a region for which the cloud DFS super master has sufficient information. For example, if agility agents and/or radar detecting sensors are distributed with a sufficient density in a given locality and the cloud DFS super master has received enough information for enough time for the locality to determine the radar signal signature for the locality with enough certainty to comply with FCC or other applicable requirements, the cloud DFS super master may provide DFS master services for devices located in the locality.

With a cloud DFS super master system, traditional DFS masters and agility agents can be eliminated or operate as sensors that continue to do radar detection, but do not tell client devices what channels to use. In this system, client devices do not have to look for a beacon, but instead can query the cloud DFS super master to determine what channels are available to use.

This cloud DFS super master systems solve several problems inherent to prior-art DFS master systems. For example, the cloud DFS super master system may receive information from external sources (such as topographic and map information from GIS servers, FCC databases, NOAA databases, DoD databases) that the cloud DFS super master uses to geo-fence an area from DFS communications in one or more channels. In one example, the DoD instructs the cloud DFS super master to prevent communications in the DFS spectrum in a given area for a time period. The cloud DFS super master system would instruct client devices not to use the DFS spectrum when the devices are in that area. In another example, the cloud DFS super master is programmed to receive a request to vacate one or more 5 GHz radio channels from a priority user. A priority user can be a radar producer that includes a system of a radar producing entity such as an airport or military body, or the priority user can be a government or emergency entity that needs priority access to the DFS spectrum. In this example, the cloud DFS super master is also programmed to transmit a message to the client devices within the affected areas of the request instructing the client devices to vacate the 5 GHz radio channels in response to the request from the priority user. Using this system, an airplane or airport could request the cloud DFS super master to block out a 5 GHz channel along its route as it is taking off. In another embodiment, the request to vacate one or more 5 GHz radio channels could come from governmental, regulatory, or emergency systems. For example, an ambulance or other emergency vehicle can send real time requests to the cloud DFS super master to block out a 5 GHz channel along its route in order to optimize communications for the emergency vehicle. Current beaconing systems cannot efficiently solve this problem unlike the disclosed cloud DFS super master. The cloud DFS super master can further receive and use location information for the priority user to dynamically change the area in which the DFS super master instructs devices to vacate the channel(s) requested by the priority user. This allows the DFS super master to geo-fence a limited area to maximize the availability of the DFS channels to other devices while still complying with the request to vacate from the priority user.

Additionally, the cloud DFS super master systems addresses current limitations of use of the DFS spectrum. Currently, many DFS master devices are private access points that only provide access to the DFS spectrum to member client devices. Accordingly, most users in the area cannot utilize the available DFS spectrum because they are not members of the group with access to the access point acting as the DFS master. In this situation, even though the DFS spectrum is unlicensed and generally available to the public for use, only a select group with access to the private access point can use the DFS spectrum. The cloud DFS super master addresses this inefficiency by providing DFS channel availability information directly to client devices in any area for which the cloud DFS super master has sufficient spectral information.

Further, the cloud DFS super master systems addresses problems with proliferation of LTE-U devices and interoperability of LTE-U devices and Wi-Fi devices. LTE-U devices use the same bands as Wi-Fi devices. However, Wi-Fi devices cannot detect LTE-U devices, and LTE-U devices cannot detect Wi-Fi devices. Consequentially, signals from LTE-U and proximate Wi-Fi devices collide and interfere with each other. The cloud DFS super master can control the timing and frequencies used by connected devices. And because the cloud DFS super master can see all of the client devices—including LTE-U and Wi-Fi devices—the cloud DFS super master can coordinate traffic to mitigate collisions for, by example, making sure that two devices in the same area are not on the same channel. The cloud DFS super master addresses the issue of proximate LTE-U and Wi-Fi devices without a need for the LTE-U and Wi-Fi devices to talk to each other.

Also, as discussed above, the cloud DFS super master solves the hidden node issue. And the cloud DFS super master can coordinate traffic among client devices.

In one embodiment of the cloud DFS super master system, the cloud DFS super master is connected to an access point that receives channel selection information from the cloud DFS super master (such as a whitelist or blacklist) and transmits beacons according to the received channel selection information. In this case the cloud DFS super master still controls the channel selection for the access point.

FIG. 17 illustrates an exemplary embodiment of the cloud DFS super master system 1700 in which the cloud intelligence engine 1735 operates as a cloud DFS super master. In the system 1700, the cloud DFS super master 1735 is communicatively coupled to a plurality of sensors 1750, 1751, 1752 that detect radar signals in the DFS band and detect wireless traffic information. The plurality of sensors 1750, 1751, 1752 may be in agility agents or may be standalone sensors. In one example, the standalone sensor includes a power supply and is self-contained in an enclosure and comprises a self-contained plug-in device. The sensors' communication with the cloud DFS super master 1735 may be continuous or intermittent. The sensors transmit information about detected radar signals, traffic information, and geo-location information for the sensor to the cloud DFS super master 1735. The cloud DFS super master 1735 may also be connected to external data sources 1760 such as topographic and map information from GIS servers, FCC databases, NOAA databases, DoD databases. The cloud DFS super master 1735 uses the information from the sensors 1750, 1751, 1752 and the external databases 1760 to determine available DFS channels for the areas for which the cloud DFS super master has sufficient information. Then as shown in FIG. 17, client devices 1780, 1781 then connect to the cloud DFS super master 1735 to request authorized DFS channels according to the location of the client devices 1780, 1781. The client devices 1780, 1781 transmit geo-location information to the cloud DFS super master 1735 so that the cloud DFS super master 1735 can determine the appropriate channels based on that location information.

In one embodiment, the cloud DFS super master system is a system for detecting radar signals and avoiding interference with the radar signals that includes a cloud DFS super master, a plurality of radar detectors, and at least one client device. The plurality of radar detectors (or radar sensors) are communicatively coupled to the cloud DFS super master and programmed to scan for a radar signal in each of a plurality of 5 GHz radio channels, to transmit the results of the scan for the radar signal to the cloud DFS super master, and to transmit geo-location information for each of the plurality of radar detectors to the cloud DFS super master. The client device (or client devices) is communicatively coupled to the cloud DFS super master and programmed to transmit geo-location information for the client device and a request for available 5 GHz radio channels to the cloud DFS super master. The cloud DFS super master is programmed to receive the results of the scan for the radar signal from each of the plurality of radar detectors, the geo-location information for the plurality of radar detectors, the geo-location information for the client device and the request for available 5 GHz radio channels and is programmed to determine one or more 5 GHz radio channels that are free of radar signals within a distance of the client device from the results of the scan for the radar signal from each of the plurality of radar detectors, the geo-location information for the plurality of radar detectors, and the geo-location information for the client device and to transmit the one or more 5 GHz radio channels that are free of radar signals within a distance of the client device to the client device.

In another embodiment, the cloud DFS super master is programmed to receive information from an external data source and is programmed to determine which of the one or more 5 GHz radio channels that are free of radar signals within a distance of the client device from the information from the external data source and the results of the scan for the radar signal from each of the plurality of radar detectors, the geo-location information for the plurality of radar detectors, and the geo-location information for the client device. The external data source can be a GIS, an FAA radar database, a DoD radar database, an FCC database, or a NOAA database for example.

Along with radar detection information, the plurality of radar detectors may be programmed to transmit wireless spectrum information (such as traffic, congestion, channels used by proximate access points) to the cloud DFS super master and the cloud DFS super master is programmed to coordinate transmissions of the client device. This way, the cloud DFS super master can coordinate traffic for several devices including access points to reduce congestion and collisions from using the same channel at the same time. The cloud DFS super master may apply time division and/or frequency division coordination to improve the client devices' performance.

As described above, the cloud DFS super master uses the location of radar detectors to determine areas in which a radar signal has been detected as well as areas that are free of radar signals. With many radar detectors distributed over a wide area, including location detection hardware and software in each radar detector may be cost prohibitive. Also, recording the location of each radar detector may also be impractical.

In one example, the distributed radar detectors disclosed herein are configured to gather location information from proximate wireless devices, such as mobile phones, and transmit that information to the cloud DFS super master. The location information from the wireless device is not necessarily the location of the radar detector. Accordingly, the cloud DFS super master aggregates location information from many wireless devices proximate to the radar detector (or multiple radar detectors), post processes and time shifts the information, and calculates the location of the radar detector(s) based on the location information from the wireless devices.

In one example, the radar detector is programmed to query nearby devices, such as phones, asking for their location. For example, proximate devices may be programmed with an application that communicates with the radar detector via Bluetooth and transmits the devices location. Bluetooth is just one example of the transmission system that may be used. In a preferred embodiment, the communication protocol is a connectionless protocol. Connectionless describes communication between two end points in which a message can be sent from one end point to another without prior arrangement. A connectionless protocol can include those using “broadcast” protocols that are not re-sent and do not have an intended recipient. Rather the broadcast is periodic, and eventually a listener passing by could pick up a message in a beacon for example. The message could be encrypted and securely hashed so that a passive listener is assured that the broadcast was authentic. Accordingly, the device at one end of the communication transmits data to the other, without first ensuring that the recipient is available and ready to receive the data. The device sending a message simply sends it addressed to the intended recipient. If there are problems with the transmission, it may be necessary to resend the data several times. Connectionless protocols are often described as stateless because the endpoints have no protocol-defined way to remember where they are in a “conversation” of message exchanges. The alternative to the connectionless approach uses connection-oriented protocols, which are sometimes described as stateful because they can keep track of a conversation. A connectionless protocol is advantageous to allow the client device to send location information to one or more radar detectors proximate to the client device. In one example, a client device transmits location information that is received by more than one radar detector. The radar detectors transmit this information to the cloud DFS super master that can use the information from both radar detectors to triangulate (and/or multiangulate, trilaterate, multilaterate) locations of the radar detectors relative to the client location information. The cloud DFS super master may also employ machine learning or artificial intelligence (“AI”) to intelligently sort and weigh the location samples.

The devices include location-detecting hardware such as GPS. The devices may use additional sensors with GPS to assist with determining location. For example, GPS may be augmented with other sensors (e.g., to aid in dead-reckoning if GPS signal is lost indoors) such as inertial sensors. The devices send their location to the radar detector. The radar detector receives multiple signals from devices all around the radar detector over time. These signals may include location information for devices proximate to the radar detector in all three dimensions (including above and below the detector). The radar detector transmits this information to the cloud DFS supper master that collects this information over time. The cloud DFS super master then averages, triangulates, multiangulates, trilaterates, multilaterates, uses machine learning or AI, and/or performs other mathematical calculations to determine the location of the radar detector. In this calculation, the cloud DFS super master may also make determinations about the value of the GPS data in the location calculation. For example, someone who came in from a parking garage adjacent to a building in which the radar detector is located may have outdated GPS information in their device. Whereas someone who just entered the building from an outside door will have more up to date information. The device may send a time stamp with the location information that the radar detector transmits to the cloud DFS super master along with a receive time stamp indicating when the radar detector received the location information. The cloud DFS super master compares the two time stamps and determines the value of the location information in determining the location of the radar detector. Because an outdated location time stamp may indicate that the device user has traveled a substantial distance since the location information was gathered, it may not be as pertinent to the location of the radar detector. Accordingly, location information that is recent may be given a higher weight in determining the location of the radar detector.

Additionally, the radar detector may transmit other information to the cloud DFS super master including information about the signal from the device including signal strength, receive strength (e.g., RX, RSSI). For example, the cloud DFS super master may determine if the signal strength is stronger or weaker, and thereby can determine whether the signal originated closer to or further away from the radar detector than other sources. Alternatively, the cloud DFS super master may determine the specific mapping or correlation between signal strength and the location of the specific device from the radar detector to estimate of the distance of each signal from the radar detector.

The cloud DFS super master may also use the time stamp from the radar detector to correlate the location information to other information the cloud DFS super master has received from external databases. For example, the cloud DFS super master may use the time of the signal to go back in historical data and look at where the corresponding satellites were located at that time of day to determine how accurate the satellites were at that time. Thereby, the cloud DFS super master may determine the appropriate weighting to give to the location information received. Additionally, the cloud DFS super master may filter the collected location information based on other information such as satellite ephemeris (wobble, where at) information.

FIG. 18 is an illustration of multiple radar detectors 1801-1804 distributed throughout a building 1800. FIG. 18 shows the floor plan of one floor of the building 1800, but in this example the building 1800 has multiple floors that are not shown. As shown, the building 1800 has stairs 1810, 1811 to floors above and below the floor shown. The building 1800 also has escalators 1812, 1813 and elevators 1814 to floors above and below that shown. The building also has a main entrance 1815 from outside. FIG. 19 illustrates the building 1800 of FIG. 18 with the distributed radar detectors 1801-1804. FIG. 19 also shows a plurality of points 1910 from which the radar detector 1803 has received location information from client devices. The radar detector 1803 receives the location information 1910 from client devices and transmits this information (including time stamps for the location information, signal strength, etc.) to the cloud DFS super master. FIG. 20 is an illustration of the location information 1910 that the cloud DFS super master receives from the radar detector 1803. The cloud DFS super master does not know beforehand the exact location 2003 of the radar detector 1803 relative to the location information 1910. The cloud DFS super master uses the location information 1910 (including time stamps, signal strength, etc.) to determine the location 2003 of the radar detector 1803.

For example, as shown in FIG. 21, the cloud DFS super master may use a boundary condition, such as the circle 2110 shown, and maximize the points of location information 1910 within the boundary 2110 and then determine the center or locus of the location information (e.g., as the mean or median location). Additionally, the cloud DFS super master may weigh the location information based on, for example, the time stamp. In on example, the stairway 1810 leads to a parking garage. The devices of people entering the parking garage via the stairway 1810 may have location information (e.g., GPS information) that is outdated and does not accurately reflect the current location of the user. The cloud DFS super master may apply a reduced weight or discard this information in determining the location of the radar detector 1803. Similarly, the devices of people entering from the outside entrance 1815 may have recent location information. And devices that are able to determine location information inside the building 1800 may also have recent location information. The cloud DFS super master may positively weight this location information in determining the location of the radar detector 1803.

Also, the cloud DFS super master may use location information receive from one or more of the radar detectors 1801-1804 to determine the location of the radar detectors. For example, the cloud DFS super master may determine that detector 1801 and detector 1803 are located in the same building. And the cloud DFS super master may determine that detector 1801 receives location information with more recent time stamps than detector 1803. From that information, the cloud DFS super master may determine detector placement information such as that detector 1801 is located near an entrance and detector 1803 is located far from entrances. Additionally, the cloud DFS super master may use traffic patterns from the location information including the times of day the location information is received. And the cloud DFS super master may use location information in three dimensions to help determine the location of radar detectors 1801-1804. For example, detector 1802 may receive location information from users leaving the elevators 1814, escalator 1812, or stairway 1810 that varies in the Z direction (or height) where the building 1800 is shown in the X-Y plane. The cloud DFS super master uses this information to determine that radar detector 1802 is located near stairs, an escalator, or elevator (or area open to multiple levels, for example).

As shown in FIG. 22, more than one radar detector may receive location information 2210 from a client device. The location information points 2210 shown in FIG. 22 represent information for which radar detectors 1803 and 1804 received location information from the same client devices. The cloud DFS super master receives the location information from both radar detectors 1803, 1804 and uses the location information to determine the location of one or more of the radar detectors 1803, 1804. The cloud DFS super master may use information from several radar detectors in a building 1800 as well as radar detectors in other locations to determine the location of a radar detector in the building 1800 (or in another location).

The cloud DFS super master may also have access to external databases (including, for example, GIS, FAA, DOD, FCC, NOAA, map databases including Google Maps, Apple Maps, Bing Maps, USGS) and use the location information received from radar detectors in conjunction with information from external databases to locate the radar detectors. For example, as shown in FIG. 19, the cloud DFS super master may get a rough location for a radar detector 1803 from location information 1910. Then, the cloud DFS super master may download building information in the area, such as building floorplan 1800. Then based on the location information 1910 including traffic patterns and determinations of features like stairs, elevators, and escalators, the cloud DFS super master orients and locates the radar detector 1803 (or multiple radar detectors) with the building information (e.g., aligning the building floorplan 1800). Thereby, the cloud DFS super master can more-accurately locate the radar detector 1803.

FIG. 23 illustrates an exemplary method 2300 of using the radar detector(s) and the coupled cloud DFS super master to determine the location of the radar detector(s). As shown, the radar detector scans for a radar signal 2301. The radar detector then transmits the results of the scan for the radar signal to the cloud DFS super master 2310. The cloud DFS super master receives the results of the scan for the radar signal from the radar detector 2320 and receives geolocation information of a client device communicatively coupled to the radar detector 2330. The cloud DFS super master integrates the client device geolocation information with other client device geolocation information to generate integrated client device geolocation information 2340. The cloud DFS super master then determines a location for the radar detector based at least on the integrated client device geolocation information 2350. Finally, the cloud DFS super master determines a radio channel free of the radar signal based at least on the location for the radar detector and the results of the scan for the radar signal 2360.

In the present specification, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. Moreover, articles “a” and “an” as used in this specification and annexed drawings should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.

In addition, the terms “example” and “such as” are utilized herein to mean serving as an instance or illustration. Any embodiment or design described herein as an “example” or referred to in connection with a “such as” clause is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the terms “example” or “such as” is intended to present concepts in a concrete fashion. The terms “first,” “second,” “third,” and so forth, as used in the claims and description, unless otherwise clear by context, is for clarity only and does not necessarily indicate or imply any order in time.

What has been described above includes examples of one or more embodiments of the disclosure. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing these examples, and it can be recognized that many further combinations and permutations of the present embodiments are possible. Accordingly, the embodiments disclosed and/or claimed herein are intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the detailed description and the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim. 

What is claimed is:
 1. A system comprising: a cloud Dynamic Frequency Selection (“DFS”) super master; a radar detector communicatively coupled to the cloud DFS super master and programmed to scan for a radar signal and to transmit radar detection results, comprising at least information associated with a radio channel that is free of the radar signal, of the scan for the radar signal to the cloud DFS super master; wherein the cloud DFS super master is programmed to receive the radar detection results of the scan for the radar signal from the radar detector and to receive geolocation information of a client device communicatively coupled to the radar detector, integrate the client device geolocation information with other client device geolocation information pertaining to the client device geolocation information to generate integrated client device geolocation information, determine a location for the radar detector based at least on the integrated client device geolocation information, and determine the radio channel that is free of the radar signal based at least on the location for the radar detector and the radar detection results of the scan for the radar signal.
 2. The system of claim 1 wherein the radar detector is programmed to receive the client device geolocation information from the client device and to transfer the client device geolocation information to the cloud DFS super master.
 3. The system of claim 2 wherein the radar detector comprises a Bluetooth transceiver and is programmed to receive the client device geolocation information from the client device via the Bluetooth transceiver.
 4. The system of claim 1 wherein the cloud DFS super master is programmed to receive the client device geolocation information from the client device.
 5. The system of claim 4 wherein the radar detector is programmed to send a location request and a radar detector identifier to the client device that causes the client device to transmit the client device geolocation information and the radar detector identifier to the cloud DFS super master.
 6. The system of claim 1 wherein the cloud DFS super master is programmed to receive information from an external data source and is programmed to determine the location of the radar detector based at least on the information from the external data source and the integrated client device geolocation information.
 7. The system of claim 6 wherein the external data source is selected from the group consisting of a Geographical Information System (GIS), an Federal Aviation Administration (FAA) radar database, a Department of Defense (DOD) radar database, an Federal Communications Commission (FCC) database, a National Oceanic and Atmospheric Administration (NOAA) database, a crowd-sourced database, and a public/safety database.
 8. The system of claim 6 wherein the external data source is a map database and the cloud DFS super master is programmed to determine the location of the radar detector based at least on the integrated client device geolocation information a map received from the map database.
 9. The system of claim 1 wherein the radar detector comprises an agility agent.
 10. The system of claim 1 wherein the radar detector comprises an access point, LTE small cell or base station, or peer to peer device.
 11. A method comprising: scanning for a radar signal with a radar detector communicatively coupled to a cloud Dynamic Frequency Selection (“DFS”) super master to generate radar detection results comprising at least information associated with a radio channel that is free of the radar signal; transmitting the radar detection results of the scan for the radar signal to the cloud DFS super master; receiving with the cloud DFS super master the radar detection results of the scan for the radar signal from the radar detector; receiving with the cloud DFS super master geolocation information of a client device communicatively coupled to the radar detector; integrating with the cloud DFS super master the client device geolocation information with other client device geolocation information pertaining to the client device geolocation information to generate integrated client device geolocation information; determining with the cloud DFS super master a location for the radar detector based at least on the integrated client device geolocation information; and determining with the cloud DFS super master the radio channel that is free of the radar signal based at least on the location for the radar detector and the radar detection results of the scan for the radar signal.
 12. The method of claim 11 further comprising receiving with the radar detector the client device geolocation information from the client device and transferring the client device geolocation information to the cloud DFS super master.
 13. The method of claim 12 further comprising receiving with the radar detector the client device geolocation information from the client device via a Bluetooth transceiver.
 14. The method of claim 11 further comprising receiving with the cloud DFS super master the client device geolocation information from the client device.
 15. The method of claim 14 further comprising sending with the radar detector a location request and a radar detector identifier to the client device that causes the client device to transmit the client device geolocation information and the radar detector identifier to the cloud DFS super master.
 16. The method of claim 11 further comprising receiving with the cloud DFS super master information from an external data source and determining the location of the radar detector based at least on the information from the external data source and the integrated client device geolocation information.
 17. The method of claim 16 wherein the external data source is selected from the group consisting of a Geographical Information System (GIS), an Federal Aviation Administration (FAA) radar database, a Department of Defense (DOD) radar database, an Federal Communications Commission (FCC) database, a National Oceanic and Atmospheric Administration (NOAA) database, a crowd-sourced database, and a public/safety database.
 18. The method of claim 16 wherein the external data source is a map database and the cloud DFS super master is programmed to determine the location of the radar detector based at least on the integrated client device geolocation information a map received from the map database.
 19. The method of claim 11 wherein the radar detector comprises an agility agent.
 20. The method of claim 11 wherein the radar detector comprises an access point, LTE small cell or base station, or peer to peer device. 